This document describes the replication files associated with the following paper:
Hirofumi Miwa, Kazuhiro Atsumi, Naofumi Fujimura, Yoshinobu Kano, and Naoto Nonaka. “Compromise or Differentiate? The Role of Partner Popularity in Coalition Parties’ Strategic Parliamentary Speech.” Journal of Legislative Studies.

Contained Files:

A1_model_evaluation.r: R script to evaluate the performance of the LightGBM classifier (Online Appendices B and D).
A2_Komeito_main.r: R script to replicate the main analysis (main text and Online Appendices C, G, and H).
A3_Komeito_2sentences.r: R script to replicate the robustness check using the last two sentences (Online Appendix D).
A4_Komeito_4sentences.r: R script to replicate the robustness check using the last four sentences (Online Appendix D).
A5_Komeito_interaction.r: R script to replicate the robustness check with an interaction between the change in LDP support and DPJ government (Online Appendix E).
A6_Komeito_government.r: R script to replicate the robustness check using only data from the LDP–Komeito government period (Online Appendix F).
A7_LDP.r: R script to replicate the analysis of LDP legislators’ speeches (Online Appendix I).
A8_DPJ.r: R script to replicate the analysis of DPJ legislators’ speeches (Online Appendix J).
A9_JCP.r: R script to replicate the analysis of JCP legislators’ speeches (Online Appendix J).
A10_functions.r: R script defining auxiliary functions.
B1_dynamic_multinomial_model.r: JAGS script to estimate the dynamic linear model.
B2_dynamic_multinomial_model_split.r: JAGS script to estimate the dynamic linear model using only data from the LDP–Komeito government period.
C1_main_data.csv: Classification results by month with LDP support rates and other covariates.
C2_feature_importance.csv: Feature importance of trigrams for the LightGBM model and the usage frequency of each feature by party.
D1_Komeito_main_result.Rdata: MCMC draws of the dynamic linear model from the analysis in A2_Komeito_main.r.
D2_Komeito_2sentences_result.Rdata: MCMC draws from A3_Komeito_2sentences.r.
D3_Komeito_4sentences_result.Rdata: MCMC draws from A4_Komeito_4sentences.r.
D4_Komeito_interaction_result.Rdata: MCMC draws from A5_Komeito_interaction.r.
D5_Komeito_government_result.Rdata: MCMC draws from A6_Komeito_government.r.
D6_LDP_result.Rdata: MCMC draws fromA7_LDP.r.
D7_DPJ_result.Rdata: MCMC draws from A8_DPJ.r.
D8_JCP_result.Rdata: MCMC draws from A9_JCP.r.
E_codebook.pdf: Documentation for C1_main_data.csv and C2_feature_importance.csv.

Additional Notes:
These materials reproduce our downstream analyses given the LightGBM-based speech classification outputs. These materials reproduce all downstream analyses from the LightGBM classification outputs. The raw texts, cleaning scripts, and training data/code are unavailable due to coauthor restrictions, so the classification step itself cannot be replicated. All subsequent analyses are fully reproducible with the files provided.

When running A2–A9, you may skip MCMC estimation to save time by loading the corresponding .Rdata files.

All analyses were conducted in R 4.3.3 with the following packages: coda 0.19–4.1, forecast 8.22.0, mltest 1.0.1, runjags 2.2.2–4, stringi 1.8.3. We fixed random seeds where applicable. Minor numerical differences may occur across operating systems/BLAS implementations, but the substantive conclusions are unchanged.

For questions about these files, please contact Hirofumi Miwa.